The user should not need a tester corpus run per symbol. Every pattern
condition in Signals\Signal{MA,RSI,MACD,Ichimoku}.mqh anchors its reads on
`int idx = StartIndex()` with zero hardcoded indices (verified), so a
name-hiding StartIndex override + EvalShift(i) on CExpertSignalCustom makes
the EXACT live ladder code answer "what would you have fired at bar i" -
the silent-divergence trap that justified the DB corpus does not exist on
this path, and neither do the GMT-offset ambiguity, the DB row caps, or
the wipe procedure.
- CExpertSignalCustom: m_evalShift + StartIndex()/EvalShift() +
SweepPrepare(bars) (deep-resizes the shared price series); the four
classic signal classes override SweepPrepare to deep-resize their own
indicator buffers.
- CSignalMETA::BuildCorpusBySweep: per bar x per source filter, run
Direction() shifted, harvest the per-side pattern slots + netVote into
the same corpus arrays the DB loader fills; entry=bar open so
MetaPrepareEra's resolution matches at offset +0 with zero price error.
DB corpus remains the fallback when classic filters are disabled.
- Warrior_EA.mq5: META gets the enabled classic filters as candidate
sources (family ids match the descriptor one-hot).
- UseDatabaseRanking default false -> true (user request): a META chart
journals + ranks out of the box.
Workflow per symbol is now: attach ONE chart with AIType=META (optionally
Meta_ExportDataset=true for the offline pool) - candidates, labels,
training and export all happen in place, ~10 seconds of sweep instead of a
tester run.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The historical 1000-row cap existed for a real reason: ProcessSignal
pulled BOTH full tables into MQL struct arrays on every buffered
signal, and UpdateSignalsWeights pulled all 52 per cycle -
materializing thousands of string-bearing structs per event is the
practical limit the cap protected against (SQLite itself has none).
Raising the cap for an 18-year meta-label corpus build would have
made runs crawl; sharding across databases would re-read the same
rows and inherit the same cost.
Every question is now answered inside SQLite, one row or one number
per query, flat in table size:
- FetchOpenTradeEntry: the open (NA) trade''s entryPrice for
pattern+direction, LIMIT 1
- FetchNewestTimeKey: newest row''s yyyymmddhhmm via max ROWID
(rows insert chronologically) - the duplicate/outdated guard
- FetchWinLossCounts: COALESCE''d SUM aggregates with the
before-now bound applied in SQL, replacing the tester-only array
trim (now also active live, where it is harmless by construction)
ProcessSignal semantics preserved exactly: prune -> close opposite
(stop-and-reverse still registers its own row) -> duplicate/outdated
-> one-open-trade -> register. CalculatePatternWinRate''s array walk
becomes WinRateFromCounts; the private FetchTradeRecords wrapper and
ShouldDeleteOldestEntry are gone. DB_MaxRowsPerTable=20000 is now
cheap at any table size.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Implements stage S1 of Meta_Labeling_Design.md, superseding the
original "training-time ladder sweep": the per-side journaling from
652bf81/195be20 already produces the exact candidate stream a sweep
would compute - every pattern instance the live ladders fire, both
sides, uncensored, with netVote and touchable entry price - so the
corpus is READ from the DB instead of re-implementing 26 ladder
conditions in training code. That eliminates the silent-divergence
trap outright: the corpus is by construction identical to live
behaviour. Accepted costs are documented in the module and the doc:
coverage equals the populating backtest, and sampling is one
candidate per fire-stretch (the right dedup for training anyway).
- Expert\AIBase\MetaCorpus.mqh: CMetaCorpus reader (52 tables ->
SMetaCandidate rows) + VerboseMode OnInit report: volume/closed/
S&R-win-rate per family, span, and the GMT->server bar-offset
match table (offsets +0..+3h) that S2''s label plumbing pins to -
measured, not assumed.
- DB_MaxRowsPerTable input (default 1000 = old MAX_TABLE_ROWS): a
corpus build raises it (e.g. 20000) so a 15-20 year backtest
isn''t pruned; wired through CExpertSignalCustom::MaxTableRows().
- Report-only stage: nothing downstream consumes the corpus yet.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The DB system logs objectively; the decision layer reads it to compute
win rates and adjust weights. The journaling path still had one
decision-layer tendril: rows were only written when the root''s
OpenLongParams()/OpenShortParams() succeeded. Those calls validate
ORDER PLACEMENT (broker stops-level, ATR warm-up, entry-mode
rejection) and their failures cluster in volatility/spread conditions,
so the gate non-randomly censored exactly those bars out of every
pattern''s win-rate sample - the same censoring class 652bf81/c8ef478
removed, one layer down. The ledger never needed placement to be
possible: entries are marked at the touchable side of the spread and
exits are same-pattern reversals, not broker fills.
Also documents netVote for what it is: a record of the decision
layer''s state at log time (per-pattern weights inside it drift as
ranking updates land), not an objective measure - the objective part
of a row is pattern/direction/price/result.
SIGNAL_DB_SEMANTICS_VERSION 4 -> 5: row populations gain the
previously censored bars, so the database re-keys.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Verification of 652bf81 on a fresh 7-month backtest DB surfaced the
last one-sided mechanism: ProcessSignal absorbed a reversing signal as
the exit of the opposite trade and skipped registering it. For pure
EVENT patterns that strictly alternate (MACD model 3, the zero-line
cross), every reversal was consumed and the whole ledger landed on
whichever side fired first - 60 Buy rows, 0 Sell rows - so the silent
side never earned a win rate and UpdateSignalsWeights() weighted the
pattern from one side only. State patterns escaped by re-firing one
bar later.
The reversal now closes the opposite trade AND registers its own row;
the existing duplicate/outdated/open-trade checks still bound the
table at one open trade per pattern+side. Row populations change
meaning, so SIGNAL_DB_SEMANTICS_VERSION 3 -> 4 re-keys the database
(the v3 file is orphaned, not wiped - schema is unchanged).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The labelMatchesVote gate compared a single last-writer-wins label
(LongCondition then ShortCondition) against the net vote sign, which
structurally censored the pattern tables: a long event co-occurring
with any short-side state model lost its label to the later writer and
was dropped, while the mirrored short event journaled fine. Ichimoku
models 0/3 and MA model 1 could not produce a row at all by
construction (MA model 1 was "revived" in 8710240 yet still could
never journal - its weight-10 vote is exactly cancelled by the
opposing Pattern_0 state), and every pattern's win rate was measured
on a with-trend-only subset - the exact statistic
UpdateSignalsWeights() feeds back into the weights, self-sealing:
no rows -> no win rate -> default weight -> still censored.
- Direction() now evaluates the two ladders separately and snapshots
each ladder's matched pattern into its own side slot; each side that
matched journals its own row. The flat-vote poisoning the old gate
fixed stays fixed: a label can no longer contradict its side.
- The filter's net vote (raw pattern-weight units) is stored as a new
netVote column - data, never a drop filter. Snapshot is keyed on the
ladder setting a label, not on its weight, so a 0%-win-rate pattern
keeps journaling and can recover.
- SIGNAL_DB_SEMANTICS_VERSION is folded unconditionally into the DB
filename fingerprint: pattern-definition changes (b2069bc, 8710240)
re-key the database instead of blending incompatible Pattern_N
populations under one key, which the input-hash fingerprint cannot
see. 7 months of mixed-semantics rows shared one file because of it.
- dbVersion 2.0 -> 3.0: schema changed, and inserts carry the new
column, so the version-mismatch folder wipe is the migration.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Three changes, one theme: the trade placed, the trade graded, and the trade
computed are now the same trade.
1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier
pair reached the LABELS only - OpenParams still placed orders at the enum
geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before
3.33*ATR above break-even" about trades the EA never placed. Published via
g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick
contract as the confidence globals, because OpenParams runs on the root signal
which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at
era 0, and the .cfg adoption a deployed model takes. Overrides both legs and
both Intelligent modes - the certificate is exact or it is nothing. TP is
ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot
reshape the certified target.
2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl -
forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a
line-for-line transcription of the host implementation (NormalizeHost /
HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the
exact moment-write ordering. The host copies remain the runtime for the DLL
and pure-MQL5 tiers and the reference the kernels must match.
Because this box has no OpenCL platform, the safety story is layered:
- shim validation: kernels compiled as C and driven against a fp64 host
transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the
frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff
0.132 vs tolerance 1.0
- in-situ self-check: each kernel is compared against its host twin ON FIRST
USE on the real device (SelfCheckBn*), covering what the shim cannot - arg
indices and buffer bindings. Any disagreement resyncs from the good copy,
latches all BN kernels off process-wide, and training continues host-side.
A transcription bug costs a warning and some speed, never a poisoned .nnw.
- sync discipline: BatchOptions is now a CBufferDouble with explicit
authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull
read-only; restores/loads/resets push; a mid-batch handover drains the
device gamma/beta accumulator into the host arrays so no sample is lost.
3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at
init (one warning instead of warning + failed Execute). Build tag bumped to
win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five
binary-changing commits.
Both build variants compile 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The barrier geometry is derived from the instrument's own excursion
distribution (stop at q75 of adverse travel, target at q50 of favourable),
and then a 1:2 floor was applied on top, raising the target to twice whatever
the stop happened to be. On SP500 H1 that pushed the target to 6.66*ATR,
reached on 3.3% of bars inside the horizon - so the label became "almost
never a win" and every topology was trained to predict an event that
essentially does not occur. A measured target has to stay measured.
The ratio never bought what it was believed to buy. A reward:risk floor does
not create expectancy; it trades hit rate against payoff at a break-even the
geometry already fixes - which this project has separately MEASURED (payoff
0.92 -> 5.72 with expectancy flat). What it did buy was two outages: four
consecutive Market validation rejections for "no trading operations" when it
rejected 100% of setups, and the label corruption above.
Removed:
- the input and the RISK_REWARD_RATIO enum (deleted, not left dangling - a
live enum with no input behind it is the shape of the stale-.set incident
that trained ~250 eras on the wrong target)
- the forced target raise in the label geometry
- the rrOK eligibility gate in the barrier-geometry scan, so every unclamped
pairing now competes on the measurement alone. Clamping stays disqualifying
for its own unrelated reason.
- the reward < minRR*risk veto in OpenParams
Kept: g_TradeRewardRiskRatio still computed and still bridged to Kelly sizing
in MoneyIntelligent - the ratio as a SIZING input was always the sound use.
Risk stays bounded where it actually is - account risk % and CRiskBudget.
The low-reachability warning survives but is re-aimed: with nothing inflating
the target, a target the market rarely reaches can only mean the horizon is
truncating the excursions the geometry is derived from.
Both build variants compile 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The 31:1 class imbalance was self-inflicted by the TARGET, not a property
of the market. Labelling only the exact bar where a ZigZag pivot confirms
gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism
this codebase accumulated sits downstream of that one choice: the
logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias
seed, balanced-accuracy-then-precision selection with its coverage floor,
the recall floor and its catch-22, the alternation gate, NMS, and the four
oversampling designs that collapsed before them.
The reference this engine is built on (references/neuronetworksbook.pdf
ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT
EXTREMUM on every bar - ~50/50 by construction, with no imbalance to
correct at all. It never had this problem because it never asked "is this
the pivot bar".
Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's
OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its
target before its stop, within a horizon. Buy = long resolves, Sell =
short resolves, Neutral = neither. Consequences:
- dir-precision in the era line stops being a proxy and becomes the win
rate of the strategy under its own exit rules.
- Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e.
~2:1 instead of 31:1. Measured and logged at the end of the prebuild.
- Spread is charged on both legs, so it is a NET win rate.
- Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches
inside one bar and the optimistic reading is how a backtested edge
becomes a live loss.
ZigZag stays as input features (EnableSwingContext) and now also supplies
the vertical barrier: the horizon is the median confirmed leg length,
snapped to a coarse ladder. Derived, not configured, and deliberately kept
out of the filename fingerprint - a filename keyed on a measured quantity
orphans a trained model the moment the measurement moves.
Removed, because the premise died with the old target:
- the alternation gate. Correct for pivot labels (a ZigZag cannot emit two
same-type pivots in a row, so a repeat was provably a false fire), and
wrong for barrier labels, which answer each bar independently. It also
took its worst consequence with it: a one-sided model previously got ONE
trade per backtest, a hard blocker on marketplace validation.
- SignalClusterWindow now defaults off - it de-duplicated repeats that are
now real trades. Kept as an opt-in display control.
- LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel.
- the era-0 output-bias seed now needs a genuinely dominant class (0.70)
rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a
correction.
Also fixed, both found while wiring the above:
1. RefreshConvergedSignal sized its buffers from a date delta
(Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training
watermark; in the tester it is loaded from a live-chart save AHEAD of
the simulated date, so the interval inverted, Bars() returned ~0, and
the buffer came out at exactly m_historyBars - deep enough for the OHLC
window and far too shallow for the Donchian-50 / 20-bar-return / SMA
extension behind it. Inference silently computed DIFFERENT features
from the ones training learned on, live as well as in the tester. Now
sized from what the feature builder actually needs.
2. The barrier horizon is resolved on the deployed path too. A deployed
model never enters Train(), so it never reached the prebuild, and
OnlineLearnStep reads the horizon as its confirmation delay - left at
the fallback it would have backpropped bars whose barriers had not
resolved. Silent lookahead in the one place that writes to a live model.
SL_Mode/TP_Mode join the weights fingerprint: they define the labels now,
so a model trained at 1:3 must never be silently reused at 1:1. This
re-keys every pre-existing model by design - none were trained on this task.
Inference census extended with the vote gate. LongCondition/ShortCondition
open with a readiness check the refresh counters never see; in the tester it
reduces to "the seeded _optcache.nnw must have LOADED", and if it did not,
every vote is hard-zeroed while the model still answers Buy. The old three
counters would have read that as "the model says Neutral" - false, and a
completely different fix. This is the leading candidate for the
zero-direction backtest and the census can now name it in one run.
Both builds compile 0 errors / 0 warnings. Forces a full retrain.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Stops keyed to the recent swing extreme make a trade's risk a function of
how far the last swing happens to sit rather than of current volatility. On
a shallow pullback the swing sits close to the fill, so the stop is tight
enough to be taken out by noise on setups that then run to target - which is
what the Perceptron's signals were showing.
SL: lowest_low/highest_high -/+ mult*ATR -> entry -/+ mult*ATR
TP: TP_PREV_SWING (opposite swing) -> removed; ATR-from-entry
SL_PREV_SWING, TP_PREV_SWING -> removed from the enums
The SL anchors to `price` (the resolved entry), not to base_price: with a
pending entry those differ by the whole entry offset, and the risk Money
sizes against is entry-to-stop.
MIN_SL_ATR_MULTIPLIER 2.0 -> 0.5. That floor existed because a swing-
anchored stop could land arbitrarily close to the entry and needed a bound
unrelated to the chosen multiple. An entry-anchored stop is exactly
mult*ATR by construction and cannot collapse, so leaving it at 2.0 would
have silently overridden SL_ATR_x1 to 2*ATR - making the input a lie AND
forcing TP >= 4*ATR just to clear the default 1:2 rejection filter. The
broker's own stop level is enforced separately and precisely by
TCAdjustStops(), so this is now a pure sanity net.
Default TP_Mode TP_PREV_SWING -> TP_ATR_x3, so SL_ATR_x1 + TP_ATR_x3 gives a
realised 3:1 against the 1:2 filter. TP_ATR_x2 would sit EXACTLY on the 2.0
boundary where price-normalization rounding alone can reject the setup; the
default leaves a deliberate gap. This is the same interaction that once
rejected 100% of setups on every symbol (see TP_INTELLIGENT_BASE_RR).
Swing validity guards now reject only when the configuration actually uses a
swing - i.e. ENTRY_PREV_SWING. Previously an unsynced or thin history
rejected EVERY trade, including configurations whose levels no longer
reference a swing at all. The guards are kept, not deleted: a bad swing must
still never reach an entry price, and iLow/iHigh are no longer called with a
possibly-negative index.
TP_INTELLIGENT stays risk-relative. Now that risk is exactly mult*ATR the
risk- and ATR-relative forms coincide, but risk-relative keeps its
reward:risk guarantee exact after the floor or TCAdjustStops widens a stop.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
DRY - topology construction
---------------------------
CSignalCONV and CSignalHYBRID each built the Conv+Pool front-end from scratch;
CSignalLSTM and CSignalHYBRID each built the LSTM stage from scratch. The
duplicates had already drifted: HYBRID guarded the LSTM step with
MathMax(1, historyBars/2), CSignalLSTM divided unguarded, so a historyBars of 1
gave two different steps for what is documented as the same layer.
Extracted AddConvPoolStage() and AddLstmStage() onto CExpertSignalAIBase. The
three overrides are now compositions:
CONV = AddConvPoolStage
LSTM = AddLstmStage
HYBRID = AddConvPoolStage && AddLstmStage
HYBRID's "matches the standalone CONV front-end exactly, then adds LSTM" is
enforced by construction instead of by comment. Took the guarded step for both.
Also fixed a descriptor leak the duplicates shared: on a failed topology.Add()
the CLayerDescription was neither owned by the array nor deleted.
Dead code
---------
- CNet::SaveCheckpoint / CNet::LoadCheckpoint (123 lines). Superseded by the
in-memory CaptureWeights/RestoreWeights pair; Network.mqh:1312 already said so
("This replaces the file-based SaveCheckpoint/LoadCheckpoint"). Zero call
sites - every remaining mention was a comment. The five comments that
referenced them have been reworded rather than left dangling.
- CExpertSignalCustom::CheckForDuplicateTrade / FindLastTradeIndex /
UpdateTradeStatusAndExit: declared, never defined anywhere, never called.
They only made it look as though duplicate-trade detection existed.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
In AI/Network.mqh, return early from InitDirectML during
tester/optimization/forward runs to prevent agent-side file-lock
failures caused by rapid stop/restart cycles accessing DLL imports.
In Expert/ExpertSignalAIBase.mqh, add MathIsValidNumber checks in
CalibratedConfidenceMagnitude and SignaledConfidence to safely handle
NaN values, and refactor ShutdownChartCleanup to accept a preserve
flag, avoiding unnecessary chart purges during tester runs for faster
shutdowns. Also add m_purgeChartOnDestruct member.
In AI/NeuronDirectML.mqh, clean up a minor comment formatting issue.
Introduce a global boolean `g_signalsVisible` to control whether signal
objects are displayed across all timeframes or hidden entirely. When
enabled, chart arrows and restore objects are set to `OBJ_ALL_PERIODS`;
otherwise they use `OBJ_NO_PERIODS`, allowing signals to be shown or
hidden at runtime without losing saved state.
Add `ShouldTraceTradeRejections()` helper that returns true only when
running in the Strategy Tester, optimization, or forward testing modes.
Use it to print diagnostic messages when trades are rejected due to a
prohibition signal or when `OpenLongParams`/`OpenShortParams` fail to
produce valid stop/take-profit levels. This provides targeted debugging
output without cluttering live trading logs.
Add freeze-level checks, no-change modification skipping, entry price routing, and per-tick/memory budget monitoring. Override trade actions (Open, Close, Reverse, TrailingStop, TrailingOrder) to validate at the final gate before sending orders.
Add MACD_FAST, MACD_SLOW, MACD_SIGNAL presets and Ichimoku Tenkan, Kijun, Senkou presets to InputEnums.mqh. All combinations are designed to satisfy the respective indicator's validation rules (fast < slow for MACD, Tenkan < Kijun < Senkou B for Ichimoku), eliminating init errors and allowing the auto-tuner to perturb settings independently.
Introduce VOTE_CLOSE_PRESETS enum with a Disabled option (value 101) that bypasses vote-driven position closing via arithmetic thresholding, removing the need for a separate boolean flag. This ensures positions exit only via stop-loss, take-profit, or trailing when disabled.
Add BeginVote/RevokeVote lifecycle hooks to ExpertSignalCustom and ExpertSignalAIBase.
Snapshot m_lastNonNeutralSignal before condition evaluation in Direction(), and restore
the snapshot if the vote is later discarded (e.g., Hybrid quorum shortfall).
Previously, a discarded vote still consumed the alternation gate, which could
permanently gate out valid signals until the opposite direction appeared.
:106 — window key changed from the 0-59 sec field to a full datetime. Fixing #1 alone would have replaced "always 0" with "cumulative average of every bar since startup", since the window still never rolled over.
:746 — restored result /= number. number was being counted and then never used, leaving a raw sum where the base class averages. MA's 60 + RSI's 100 = 160 tripped the ±100 range check and got zeroed — it discarded precisely the strongest agreed setups.
The previous TP calculation used ATR from entry, decoupled from the swing-anchored SL distance. This caused the Min_Risk_Reward_Ratio rejection filter to always fail because reward < 2*risk with default settings, preventing any trades. Now TP is a multiple of the actual trade risk (entry-to-stop distance), restoring coupling and ensuring the default RR filter is satisfiable. Also enforce minimum SL distance before TP calculation to maintain correct risk-ratio.
Replace single `m_pattern_0` weight with four confidence-tier weights (`m_pattern_0` through `m_pattern_3`) so that `UpdateSignalsWeights()` blends across multiple patterns like classic indicators. Previously, a single pattern caused the same win rate to be written to both the pattern weight and module weight, resulting in a quadratic derating (e.g., 70% win rate scored as 49 instead of 70). The new tiers bucket confidence into four equal bands between the minimum AI confidence and 1.0, with defaults 80/87/93/100. Added `ConfidenceTier()` and `PatternWeightForTier()` helpers, and changed default pattern count from 1 to 4.
- Added optional `weighScale` parameter (default -1.0) to `CNeuronBase::Init` and `CLayer::CreateElement`.
- Updated `CNeuronPool::Init` to use LeCun-uniform scaling (1/sqrt(window+1)) for its base initialization.
- Updated `CNet::CNet` to use He-scaled initialization (sqrt(2/neurons)) for dense layers.
- These changes enable more flexible and statistically sound weight initialization, matching the rationale used in OCL-based implementations, leading to better training stability and convergence.
Replace old ATR_MULTIPLIER, THRESHOLDS_PRESET enums with new
STOP_LOSS_MODE, TAKE_PROFIT_MODE, AI_EXIT_MODE enums that support
ATR-based, intelligent confidence-scaled, and swing-anchored modes.
Also fix LSTM signal identity string.
Optimize() scaled lot size off account trade-history streaks with no Magic-number
filter (picked up other EAs'/manual trades) and an unconfigurable m_factor stuck at
1.0 (Factor() was never wired from an input), so a 3-trade streak could triple lot
size or send it negative. It was also entirely disconnected from what the AI model
actually knows about the current setup.
Replaced both AdjustRiskAmount()'s linear confidence-only scale and Optimize()'s
streak multiplier with one edge-based model: p from the empirically calibrated
AI/DB confidence magnitude, b from the trade's real reward:risk ratio (newly
bridged from OpenParams() via g_TradeRewardRiskRatio), quarter-Kelly applied and
clamped so risk% can only ever scale down from its configured ceiling, never above it.
Replace the hardcoded `MAX_CLASS_SAMPLE_WEIGHT` (3.0) with a tunable member variable `m_maxClassSampleWeight`, controlled by the new `CLASS_SAMPLE_WEIGHT_PRESET` input enum. This allows adjusting the ceiling on rare-class sample weights to prevent the previously observed anti-correlated Buy/Sell recall whipsaw (where a high multiplier caused one era's gradients to overwrite another class's separability). Default is CSW_15 (1.5x), which is gentler than the old 3.0x; lower values down to 1.0x disable rebalancing to train on raw label distribution, while higher values (up to 3.0x) converge faster but risk instability. This tuning can now be done without recompiling.
- Define MAX_WEIGHT constant (1.0e6) for weight limits in clusters
- Remove redundant barrier from FeedForward kernel (prevents sync issues)
- Port FeedForwardProof and CalcInputGradientProof kernels for max-pooling (no weights, sliding max)
- Port FeedForwardConv kernel for convolution layers (shared weights, multiple output channels)
- Remove unused code and refactor signal condition logic (CSignalPAI)
